Determining the Impact of a Physiotherapist-Led Primary Care Model for Low Back Pain: Protocol and Analysis Plan for a Cluster Randomized Controlled Trial and Embedded Process Evaluation (Preprint)
Bibliographic record
Abstract
BACKGROUND Low back pain (LBP) is a common and disabling condition that is costly for health systems and society. Interprofessional primary care models may improve care quality and reduce this burden. OBJECTIVE This protocol and analysis plan communicates the methods for a cluster randomized trial with the following objectives: (1) evaluate the effectiveness of a physiotherapist-led (PT-led) primary care model for LBP at improving disability (primary outcome), pain intensity, quality of life, global rating of change, patient satisfaction, and adverse events compared with usual physician-led primary care; and (2) determine the impact of the PT-led primary care model for LBP on the health care system and society (health care access, health care use, missed work, cost-effectiveness). Both objectives are evaluated over a 1-year period. A multimethod process evaluation is embedded to assess model implementation, mechanisms, perspectives of patients and providers, and contextual influences. METHODS This study is a cluster randomized controlled trial with 20 primary care practices (clusters) in Canada, randomized 1:1 to a PT-led or usual physician-led primary care model for LBP. Adults seeking care from their primary care team for LBP are recruited over 1 year. Data collection occurs at baseline, 6 weeks, and 3, 6, 9, and 12 months. Effectiveness will be analyzed using linear mixed regression. The process evaluation analysis will include: descriptive and comparative analyses to assess implementation; descriptive and mediation analyses to assess potential mechanisms; qualitative interpretive description to understand experiences and perspectives of patients, PTs, and other health professionals; and mixed methods to determine contextual influences on implementation. RESULTS Recruitment of primary care sites (clusters) was completed in June 2023, following delays related to the COVID-19 pandemic. Cluster randomization occurred in July 2023. Recruitment of patient participants began in October 2023 and concluded in November 2024 (n=739). The final self-reported patient data was collected on November 25, 2025. Extraction of electronic health record data is scheduled for completion on December 19, 2025. Data analysis will be conducted in accordance with the study protocol and analysis plan and will begin once all data collection activities are complete. No interim analyses have been performed. CONCLUSIONS The results of this trial will provide evidence for knowledge users to determine whether a PT-led primary care model for LBP is effective and should be adopted more widely. Knowledge users have identified the impact of the new model of care on disability, quality of life, and cost-effectiveness as key evidence needed to inform key decision-making. The multimethod process evaluation will provide critical evidence to interpret trial results and inform future scale and spread of this model of care if effective. CLINICALTRIAL ClinicalTrials.gov NCT04287413; https://clinicaltrials.gov/study/NCT04287413 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/89004
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.123 | 0.148 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".